Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11356-023-27320-7.
Artistic style transfer is to render an image in the style of another image, which is a challenge problem in both image processing and arts. Deep neural networks are adopted to artistic style transfer and achieve remarkable success, such as AdaIN (adaptive instance normalization), WCT (whitening and coloring transforms), MST (multimodal style transfer), and SEMST (structure-emphasized multimodal style transfer). These algorithms modify the content image as a whole using only one style and one algorithm, which is easy to cause the foreground and background to be blurred together. In this paper, an iterative artistic multi-style transfer system is built to edit the image with multiple styles by flexible user interaction. First, a subjective evaluation experiment with art professionals is conducted to build an open evaluation framework for style transfer, including the universal evaluation questions and personalized answers for ten typical artistic styles. Then, we propose the interactive artistic multi-style transfer system, in which an interactive image crop tool is designed to cut a content image into several parts. For each part, users select a style image and an algorithm from AdaIN, WCT, MST, and SEMST by referring to the characteristics of styles and algorithms summarized by the evaluation experiments. To obtain richer results, the system provides a semantic-based parameter adjustment mode and the function of preserving colors of content image. Finally, case studies show the effectiveness and flexibility of the system.
It is widely considered that weather conditions affect the spread of COVID-19, but to date, the collective influence of demographic factors and government policy response measures have hardly been considered. The objective of this study is to utilize a machine learning method to assess the corresponding roles of meteorological variables, demographic factors, and government response measures in daily new cases of COVID-19 among multiple climate zones at city/county level. The overall model showed good performance with a validated R2 of 0.86, as satisfactory as individual climate zone models. Population density ranked the most important factor, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated among meteorological factors, but the association with daily new cases seemed to be inconsistent among different climate zones. Implementing stricter response measures could help effectively contain the spread of COVID-19, but did so with a lagged effect, and the typical lockdown measures might not be applicable to all climate conditions. This study preliminarily analyzed the roles of certain factors in the transmission of COVID-19, and provided practical evidence for developing an early health warning system of global pandemics by leveraging big data technology and multiple sourced data fusion.
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